import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

__a    :	Union[str, Any]         =    random.Random()





def 							UpperCAmelCase		(       lowercase				,     lowercase=1.0				,     lowercase=None				,     lowercase=None				):







				"""simple docstring"""



				if rng is None:
								__lowercase					     =				global_rng

				__lowercase					     =				[]
				for batch_idx in range(shape[0]				):
								values.append([]				)
								for _ in range(shape[1]				):
												values[-1].append(rng.random() * scale				)

				return values




@require_torch
@require_torchaudio
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=7 ,  lowerCAmelCase__=4_00 ,  lowerCAmelCase__=20_00 ,  lowerCAmelCase__=24 ,  lowerCAmelCase__=24 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=1_60_00 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  )	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				min_seq_length
							__lowercase					     =				max_seq_length
							__lowercase					     =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__lowercase					     =				feature_size
							__lowercase					     =				num_mel_bins
							__lowercase					     =				padding_value
							__lowercase					     =				sampling_rate
							__lowercase					     =				return_attention_mask
							__lowercase					     =				do_normalize





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return {
							    "feature_size": self.feature_size,
							    "num_mel_bins": self.num_mel_bins,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__=False ,  lowerCAmelCase__=False		)	->   Union[str, Any]:

							'''simple docstring'''


							def _flatten(lowerCAmelCase__		):
											return list(itertools.chain(*lowerCAmelCase__		)		)

							if equal_length:
											__lowercase					     =				[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
							else:
											# make sure that inputs increase in size
											__lowercase					     =				[
											    floats_list((x, self.feature_size)		)
											    for x in range(self.min_seq_length ,  self.max_seq_length ,  self.seq_length_diff		)
											]
							if numpify:
											__lowercase					     =				[np.asarray(lowerCAmelCase__		) for x in speech_inputs]
							return speech_inputs




@require_torch
@require_torchaudio
class   _UpperCamelCase (		_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Dict     					=		SpeechaTextFeatureExtractor if is_speech_available() else None





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				SpeechaTextFeatureExtractionTester(self		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   List[str]:

							'''simple docstring'''


							self.assertTrue(np.all(np.mean(lowerCAmelCase__ ,  axis=0		) < 1E-3		)		)
							self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__ ,  axis=0		) - 1		) < 1E-3		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase					     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							# create three inputs of length 800, 1000, and 1200
							__lowercase					     =				[floats_list((1, x)		)[0] for x in range(8_00 ,  14_00 ,  2_00		)]
							__lowercase					     =				[np.asarray(lowerCAmelCase__		) for speech_input in speech_inputs]

							# Test feature size
							__lowercase					     =				feature_extractor(lowerCAmelCase__ ,  padding=lowerCAmelCase__ ,  return_tensors='''np'''		).input_features
							self.assertTrue(input_features.ndim == 3		)
							self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size		)

							# Test not batched input
							__lowercase					     =				feature_extractor(speech_inputs[0] ,  return_tensors='''np'''		).input_features
							__lowercase					     =				feature_extractor(np_speech_inputs[0] ,  return_tensors='''np'''		).input_features
							self.assertTrue(np.allclose(lowerCAmelCase__ ,  lowerCAmelCase__ ,  atol=1E-3		)		)

							# Test batched
							__lowercase					     =				feature_extractor(lowerCAmelCase__ ,  return_tensors='''np'''		).input_features
							__lowercase					     =				feature_extractor(lowerCAmelCase__ ,  return_tensors='''np'''		).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__ ,  lowerCAmelCase__		):
											self.assertTrue(np.allclose(lowerCAmelCase__ ,  lowerCAmelCase__ ,  atol=1E-3		)		)

							# Test 2-D numpy arrays are batched.
							__lowercase					     =				[floats_list((1, x)		)[0] for x in (8_00, 8_00, 8_00)]
							__lowercase					     =				np.asarray(lowerCAmelCase__		)
							__lowercase					     =				feature_extractor(lowerCAmelCase__ ,  return_tensors='''np'''		).input_features
							__lowercase					     =				feature_extractor(lowerCAmelCase__ ,  return_tensors='''np'''		).input_features
							for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__ ,  lowerCAmelCase__		):
											self.assertTrue(np.allclose(lowerCAmelCase__ ,  lowerCAmelCase__ ,  atol=1E-3		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							__lowercase					     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							__lowercase					     =				[floats_list((1, x)		)[0] for x in range(8_00 ,  14_00 ,  2_00		)]

							__lowercase					     =				['''longest''', '''max_length''', '''do_not_pad''']
							__lowercase					     =				[None, 16, None]
							for max_length, padding in zip(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				feature_extractor(
											    lowerCAmelCase__ ,  padding=lowerCAmelCase__ ,  max_length=lowerCAmelCase__ ,  return_attention_mask=lowerCAmelCase__		)
											__lowercase					     =				inputs.input_features
											__lowercase					     =				inputs.attention_mask
											__lowercase					     =				[np.sum(lowerCAmelCase__		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							__lowercase					     =				[floats_list((1, x)		)[0] for x in range(8_00 ,  14_00 ,  2_00		)]

							__lowercase					     =				['''longest''', '''max_length''', '''do_not_pad''']
							__lowercase					     =				[None, 16, None]
							for max_length, padding in zip(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				feature_extractor(
											    lowerCAmelCase__ ,  max_length=lowerCAmelCase__ ,  padding=lowerCAmelCase__ ,  return_tensors='''np''' ,  return_attention_mask=lowerCAmelCase__		)
											__lowercase					     =				inputs.input_features
											__lowercase					     =				inputs.attention_mask
											__lowercase					     =				[np.sum(lowerCAmelCase__		) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]		)
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							__lowercase					     =				[floats_list((1, x)		)[0] for x in range(8_00 ,  14_00 ,  2_00		)]
							__lowercase					     =				feature_extractor(
							    lowerCAmelCase__ ,  padding='''max_length''' ,  max_length=4 ,  truncation=lowerCAmelCase__ ,  return_tensors='''np''' ,  return_attention_mask=lowerCAmelCase__ ,  )
							__lowercase					     =				inputs.input_features
							__lowercase					     =				inputs.attention_mask
							__lowercase					     =				np.sum(attention_mask == 1 ,  axis=1		)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
							self._check_zero_mean_unit_variance(input_features[1]		)
							self._check_zero_mean_unit_variance(input_features[2]		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							__lowercase					     =				[floats_list((1, x)		)[0] for x in range(8_00 ,  14_00 ,  2_00		)]
							__lowercase					     =				feature_extractor(
							    lowerCAmelCase__ ,  padding='''longest''' ,  max_length=4 ,  truncation=lowerCAmelCase__ ,  return_tensors='''np''' ,  return_attention_mask=lowerCAmelCase__ ,  )
							__lowercase					     =				inputs.input_features
							__lowercase					     =				inputs.attention_mask
							__lowercase					     =				np.sum(attention_mask == 1 ,  axis=1		)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
							self._check_zero_mean_unit_variance(input_features[2]		)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape ,  (3, 4, 24)		)

							__lowercase					     =				[floats_list((1, x)		)[0] for x in range(8_00 ,  14_00 ,  2_00		)]
							__lowercase					     =				feature_extractor(
							    lowerCAmelCase__ ,  padding='''longest''' ,  max_length=16 ,  truncation=lowerCAmelCase__ ,  return_tensors='''np''' ,  return_attention_mask=lowerCAmelCase__ ,  )
							__lowercase					     =				inputs.input_features
							__lowercase					     =				inputs.attention_mask
							__lowercase					     =				np.sum(attention_mask == 1 ,  axis=1		)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]		)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]		)
							self._check_zero_mean_unit_variance(input_features[2]		)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape ,  (3, 6, 24)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							import torch

							__lowercase					     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							__lowercase					     =				np.random.rand(1_00 ,  32		).astype(np.floataa		)
							__lowercase					     =				np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											__lowercase					     =				feature_extractor.pad([{'''input_features''': inputs}] ,  return_tensors='''np'''		)
											self.assertTrue(np_processed.input_features.dtype == np.floataa		)
											__lowercase					     =				feature_extractor.pad([{'''input_features''': inputs}] ,  return_tensors='''pt'''		)
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							from datasets import load_dataset

							__lowercase					     =				load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,  '''clean''' ,  split='''validation'''		)
							# automatic decoding with librispeech
							__lowercase					     =				ds.sort('''id'''		).select(range(lowerCAmelCase__		)		)[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				np.array([
							    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
							    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
							    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
							]		)
							# fmt: on

							__lowercase					     =				self._load_datasamples(1		)
							__lowercase					     =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
							__lowercase					     =				feature_extractor(lowerCAmelCase__ ,  return_tensors='''pt'''		).input_features
							self.assertEquals(input_features.shape ,  (1, 5_84, 24)		)
							self.assertTrue(np.allclose(input_features[0, 0, :30] ,  lowerCAmelCase__ ,  atol=1E-4		)		)
from typing import Any




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__		)	->   int:

							'''simple docstring'''


							__lowercase					     =				data
							__lowercase					     =				None





			def __repr__(  self		)	->   str:

							'''simple docstring'''


							return F"Node({self.data})"




class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				None





			def __iter__(  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				self.head
							while node:
											yield node.data
											__lowercase					     =				node.next





			def __len__(  self		)	->   int:

							'''simple docstring'''


							return sum(1 for _ in self		)





			def __repr__(  self		)	->   str:

							'''simple docstring'''


							return "->".join([str(lowerCAmelCase__		) for item in self]		)





			def __getitem__(  self ,  lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							if not 0 <= index < len(self		):
											raise ValueError('''list index out of range.'''		)
							for i, node in enumerate(self		):
											if i == index:
															return node
							return None





			def __setitem__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							if not 0 <= index < len(self		):
											raise ValueError('''list index out of range.'''		)
							__lowercase					     =				self.head
							for _ in range(lowerCAmelCase__		):
											__lowercase					     =				current.next
							__lowercase					     =				data





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							self.insert_nth(len(self		) ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							self.insert_nth(0 ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   None:

							'''simple docstring'''


							if not 0 <= index <= len(self		):
											raise IndexError('''list index out of range'''		)
							__lowercase					     =				Node(lowerCAmelCase__		)
							if self.head is None:
											__lowercase					     =				new_node
							elif index == 0:
											__lowercase					     =				self.head  # link new_node to head
											__lowercase					     =				new_node
							else:
											__lowercase					     =				self.head
											for _ in range(index - 1		):
															__lowercase					     =				temp.next
											__lowercase					     =				temp.next
											__lowercase					     =				new_node





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   None:  # print every node data

							'''simple docstring'''


							print(self		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							return self.delete_nth(0		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:  # delete from tail

							'''simple docstring'''


							return self.delete_nth(len(self		) - 1		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ = 0		)	->   Any:

							'''simple docstring'''


							if not 0 <= index <= len(self		) - 1:  # test if index is valid
											raise IndexError('''List index out of range.'''		)
							__lowercase					     =				self.head  # default first node
							if index == 0:
											__lowercase					     =				self.head.next
							else:
											__lowercase					     =				self.head
											for _ in range(index - 1		):
															__lowercase					     =				temp.next
											__lowercase					     =				temp.next
											__lowercase					     =				temp.next.next
							return delete_node.data





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   bool:

							'''simple docstring'''


							return self.head is None





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   None:

							'''simple docstring'''


							__lowercase					     =				None
							__lowercase					     =				self.head

							while current:
											# Store the current node's next node.
											__lowercase					     =				current.next
											# Make the current node's next point backwards
											__lowercase					     =				prev
											# Make the previous node be the current node
											__lowercase					     =				current
											# Make the current node the next node (to progress iteration)
											__lowercase					     =				next_node
							# Return prev in order to put the head at the end
							__lowercase					     =				prev





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				LinkedList()
				assert linked_list.is_empty() is True
				assert str(lowercase				) == ""

				try:
								linked_list.delete_head()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				try:
								linked_list.delete_tail()
								raise AssertionError  # This should not happen.
				except IndexError:
								assert True  # This should happen.

				for i in range(10				):
								assert len(lowercase				) == i
								linked_list.insert_nth(lowercase				,     i + 1				)
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(1				,     11				)				)

				linked_list.insert_head(0				)
				linked_list.insert_tail(11				)
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(0				,     12				)				)

				assert linked_list.delete_head() == 0
				assert linked_list.delete_nth(9				) == 10
				assert linked_list.delete_tail() == 11
				assert len(lowercase				) == 9
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(1				,     10				)				)

				assert all(linked_list[i] == i + 1 for i in range(0				,     9				)				) is True

				for i in range(0				,     9				):
								__lowercase					     =				-i
				assert all(linked_list[i] == -i for i in range(0				,     9				)				) is True

				linked_list.reverse()
				assert str(lowercase				) == "->".join(str(lowercase				) for i in range(-8				,     1				)				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				[
				    -9,
				    100,
				    Node(77345112				),
				    '''dlrow olleH''',
				    7,
				    5555,
				    0,
				    -192.55555,
				    '''Hello, world!''',
				    77.9,
				    Node(10				),
				    None,
				    None,
				    12.20,
				]
				__lowercase					     =				LinkedList()

				for i in test_input:
								linked_list.insert_tail(lowercase				)

				# Check if it's empty or not
				assert linked_list.is_empty() is False
				assert (
				    str(lowercase				) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
				    "-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the head
				__lowercase					     =				linked_list.delete_head()
				assert result == -9
				assert (
				    str(lowercase				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None->12.2"
				)

				# Delete the tail
				__lowercase					     =				linked_list.delete_tail()
				assert result == 12.2
				assert (
				    str(lowercase				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None->None"
				)

				# Delete a node in specific location in linked list
				__lowercase					     =				linked_list.delete_nth(10				)
				assert result is None
				assert (
				    str(lowercase				) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
				    "Hello, world!->77.9->Node(10)->None"
				)

				# Add a Node instance to its head
				linked_list.insert_head(Node('''Hello again, world!'''				)				)
				assert (
				    str(lowercase				)
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
				)

				# Add None to its tail
				linked_list.insert_tail(lowercase				)
				assert (
				    str(lowercase				)
				    == "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
				    "7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
				)

				# Reverse the linked list
				linked_list.reverse()
				assert (
				    str(lowercase				)
				    == "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
				    "7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				from doctest import testmod

				testmod()

				__lowercase					     =				LinkedList()
				linked_list.insert_head(input('''Inserting 1st at head '''				).strip()				)
				linked_list.insert_head(input('''Inserting 2nd at head '''				).strip()				)
				print('''\nPrint list:'''				)
				linked_list.print_list()
				linked_list.insert_tail(input('''\nInserting 1st at tail '''				).strip()				)
				linked_list.insert_tail(input('''Inserting 2nd at tail '''				).strip()				)
				print('''\nPrint list:'''				)
				linked_list.print_list()
				print('''\nDelete head'''				)
				linked_list.delete_head()
				print('''Delete tail'''				)
				linked_list.delete_tail()
				print('''\nPrint list:'''				)
				linked_list.print_list()
				print('''\nReverse linked list'''				)
				linked_list.reverse()
				print('''\nPrint list:'''				)
				linked_list.print_list()
				print('''\nString representation of linked list:'''				)
				print(lowercase				)
				print('''\nReading/changing Node data using indexing:'''				)
				print(F"Element at Position 1: {linked_list[1]}"				)
				__lowercase					     =				input('''Enter New Value: '''				).strip()
				print('''New list:'''				)
				print(lowercase				)
				print(F"length of linked_list is : {len(lowercase				)}"				)


if __name__ == "__main__":
		main()